Summary

In this chapter, you learned about some of the most important concepts and techniques that help you in preserving privacy and ensuring security including data encryption techniques, homomorphic encryption, differential privacy, and federated learning. You learned how homomorphic encryption provides the possibility of different types of operation and machine learning inference compared to traditional data encryption techniques. You also learned how we can ensure data privacy by adding noise to the data, in differential privacy, or work with decentralized data and omit the need to transfer raw data, as in federated learning. You also practiced some of them in Python. This knowledge could be a starting point for you to learn about these concepts further and benefit from them in your machine learning projects.

In the next chapter, you will learn about the importance of integrating human feedback into machine learning modeling and the techniques that will help you on this topic...